Security and military forces operating in urban environments need the capability to detect slow moving personnel inside buildings. To identify moving personnel inside buildings, a time-domain approach may be used that uses a low-frequency, ultrawideband (UWB) radar. A low-frequency, UWB radar is desired since the low-frequency transmit pulse is capable of penetrating through the wall (1) and the UWB corresponds to a high range resolution that gives the capability to better locate the moving target (MT). The publication entitled “An Analysis of Clustering Tools for Moving Target Indication,” by Anthony Martone, Roberto Innocenti, and Kenneth Ranney, Sensors and Electron Devices Directorate, Army Research Laboratory, Adelphi, Md., ARL-TR-5037, November 2009 (hereinafter ARL Technical Report 5037), hereby incorporated by reference, discloses a description of a moving target indication (MTI) processing approach to detect and track slow-moving targets inside buildings, which successfully detected moving targets (MTs) from data collected by a low-frequency, ultrawideband radar. MTI processing algorithms include change detection (CD), used to identify the MT signature; automatic target detection (ATD), used to eliminate imaging artifacts and potential false alarms due to target multi-bounce effects; clustering, used to identify a centroid for each cluster in the ATD output images; and tracking, used to establish a trajectory of the MT. These algorithms can be implemented in a real-time or near-real-time system; however, a person-in-the-loop is needed to select input parameters for the clustering algorithm. Specifically, the number of clusters to input into the cluster algorithm is unknown and requires manual selection. As reported in ARL Technical Report 5037 (page 1), the algorithms in the MTI processing formulation can be implemented in a real-time or near real-time system; however, a person-in-the-loop is needed to select input parameters for the k-Means clustering algorithm. Specifically, the number of clusters input into the k-Means routine is unknown and requires manual selection. In the ARL Technical Report 5037, two techniques are investigated that automatically determine the number of clusters: the knee-point (KP) algorithm and the recursive pixel finding (RPF) algorithm. The KP algorithm is a well-known heuristic approach for determining the number of clusters. The RPF algorithm is analogous to the image processing, pixel labeling procedure. Both routines processed data collected by low-frequency, ultrawideband radar.
In research reported in Martone, A.; et al., “Through the Wall Detection of Slow Moving Personnel,” Proceedings of the SPIE Conference on Radar Sensor Technology XIII, vol. 7308, Orlando, Fl, April 2009, and Martone, A. et al., “Moving Target Indication for Transparent Urban Structures,” ARL-TN-4809, U.S. Army Research Laboratory: Adelphi, Md., May 2009, the effectiveness of time-domain, moving target indication (MTI) approach was reported for detecting moving personnel inside wood and cinderblock structures, moving personnel walking in nonlinear trajectories, and multiple moving personnel walking in linear trajectories.
A time-domain approach to MTI was considered as an alternative to a frequency-domain approach, i.e., Doppler processing, since a very small Doppler shift in backscattered frequency is generated due to (1) the slow motion of the mover and (2) the low frequency needed to penetrate through the wall. The reported time-domain processing algorithms are based on the change detection (CD) paradigm, which is inherently similar to clutter cancellation. In the CD paradigm, the Synchronous Impulse Reconstructive (SIRE) radar remains stationary and generates a set of images for a region of interest (ROI). Each image in the set is formed every two-thirds of a second. The stationary objects in the building remain in the same location in each image; however, moving personnel will be at different locations. The moving personnel can be detected by subtracting adjacent images in the set, thereby eliminating the stationary objects and identifying the MT signature. Additional processing is needed to enhance the MT signature and includes a constant false alarm rate (CFAR) algorithm, morphological processing, k-Means clustering, and a tracking algorithm. CFAR and morphological processing are approaches used to eliminate imaging artifacts and potential false alarms due to target multi-bounce effects. The k-Means clustering algorithm is used to identify centroids for given input clusters, where the clusters are produced by the CFAR and morphological processing algorithms. The tracker is used to establish a trajectory of the MT based on the input centroids.
By way of background, Synchronous Impulse Reconstruction (SIRE) Radar is a low-frequency, ultra-wideband (UWB) radar having a frequency range of 300 MHz˜3 GHz. An example of SIRE system is illustrated in FIG. 1, showing 2 transmitters and 16 receivers in an antenna array 2 m wide having an average power of 5 mW with a downrange swath is 10-meters and a downrange resolution is 0.056 meters.